With the rapid development of electric vehicles (EVs) in Chinese cities, accurately forecasting the number of EVs used by urban residents in the next five years and more long term is beneficial for the government to adjust industrial policies of EVs, guide the rational planning of urban charging facilities and supporting distribution network, and achieve the rational and orderly development of the EV industry. The paper considers the advantages of using the grey GM(1,1) prediction model to predict the short-term ownership of EVs by urban residents. Then, by forecasting the number of EV users in a certain city in the future and predicting the number of private vehicles in the future, the boundary conditions for long-term year ownership of EVs by residents are determined. Combined with historical data and short-term forecast data generated by the grey prediction model, the model parameters that include the innovation coefficient and imitation coefficient of the Bass model are trained using a genetic algorithm. Finally, the Bass model is used for medium- to long-term ownership forecasting from 2023 to 2040. The prediction error for the target year is provided. The simulation results indicate that the ownership of resident EVs in this city will experience rapid growth in the next five years.

The construction of community charging facilities and supporting distribution networks based on the predicted results of electric vehicle (EV) charging power in saturation year has resulted in a large initial idleness of the distribution network and a serious waste of assets. To solve this problem, this paper proposes a collaborative planning method for urban community charging facilities and distribution networks. First, based on the load density method and occupancy rate to predict the base electricity load in the community, the Bass model and charging probability are used to predict the community’s electric vehicle charging load. Taking the minimum annual construction and operation costs of the community distribution network as the objective function, the power supply topology of the distribution network for a new community is optimized by using Prim and single-parent genetic algorithms. Finally, the proposed scheme is verified by using the actual community data of a certain city in China as an analysis example, and the scheme of one-time planning of the distribution network and yearly construction of charging facilities is given.

The increase in demand for the fast charging of electric vehicles (EVs) has promoted research and the application of high-power direct current (DC) fast charging stations, and research on the stability control of charging stations with a parallel structure of multi-controllable rectifier modules is of great significance. Taking into account the dynamic and steady-state performance, the parameters of the dual-loop controller based on virtual impedance control are designed to realize the power sharing of multi-controllable rectifier modules. The constant power load model of EV charging at a constant current is established, and the load capacity of the cascaded system of converters is studied by using the impedance analysis method. The experimental and simulation results verify the effectiveness and correctness of the power sharing control strategy and the cascaded system stability analysis.